Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays

Li, Liangliang; Jian, Jigui

doi:10.1016/j.amc.2015.06.022

Cited by 8 publications

(4 citation statements)

References 47 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where (0, 0, 0, 0) = (0, 0, 0, 0) = 0. Suppose that there exist nonnegative constants , , , and such that inequalities (5) and (7) hold:…”

Section: )mentioning

confidence: 99%

“…There will be a distribution of conduction velocities along these pathways and a distribution of propagation be designed with discrete delays. Therefore, the more appropriate way is to incorporate continuously distributed delays [3][4][5][6][7]. In [8][9][10][11], the authors have studied several kinds of complex-valued neural networks with continuously distributed delays.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mean Square Exponential Stability of Stochastic Complex‐Valued Neural Networks with Mixed Delays

Yang

2019

Complexity

View full text Add to dashboard Cite

This paper investigates the mean square exponential stability problem of a class of complex-valued neural networks with stochastic disturbance and mixed delays including both time-varying delays and continuously distributed delays. Under different assumption conditions concerning stochastic disturbance term from the existing ones, some sufficient conditions are derived for assuring the mean square exponential stability of the equilibrium point of the system based on the vector Lyapunov function method and Ito^ differential-integral theorem. The obtained results not only generalize the existing ones, but also reduce the conservatism of the previous stability results about complex-valued neural networks with stochastic disturbances. Two numerical examples with simulation results are given to verify the feasibility of the proposed results.

show abstract

“…where (0, 0, 0, 0) = (0, 0, 0, 0) = 0. Suppose that there exist nonnegative constants , , , and such that inequalities (5) and (7) hold:…”

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mean Square Exponential Stability of Stochastic Complex‐Valued Neural Networks with Mixed Delays

Yang

2019

Complexity

View full text Add to dashboard Cite

show abstract

“…stochastic) BAMs to describe the the fuzzy transmission of information (aftereffect or memory, resp. stochastic perturbations) in the concerned BAMNs; see [4] and the vast references therein for more detail on the physical background of various BAMNs.…”

Section: Introductionmentioning

confidence: 99%

Mean-square exponential stability of fuzzy stochastic BAM networks with hybrid delays

Wang

2018

Adv Differ Equ

View full text Add to dashboard Cite

We study fuzzy stochastic bidirectional associative memory cellular neural networks with discrete delays in leakage terms and with continuous and infinitely distributed delays in the transmission terms. Under certain structural assumptions, we prove that the networks in question are mean-square exponentially stable. Our main ingredient is the classical direct Lyapunov approach, in which we construct an elaborate Lyapunov-Krasovskii function. The arguments in the paper can be readily adapted to study stability problems for other cellular neural networks.

show abstract

“…It is well known that a neural network usually has a spatial nature due to the presence of an amount of parallel pathways of a variety of axon sizes and lengths; it is desired to model them by introducing continuously distributed delays over a certain duration of time such that the distant past has less influence compared with the recent behavior of the state [2]. Therefore, it is necessary and accepted to study a neural networks model with both time-varying delays and continuously distributed delays, such as in [2,9,10,15,[24][25][26]. The existing results on the dynamical behavior analysis for the neural networks models with the above mixed delays were mainly with respect to real-valued neural networks.…”

Section: Introductionmentioning

confidence: 99%

Impulsive Disturbances on the Dynamical Behavior of Complex-Valued Cohen-Grossberg Neural Networks with Both Time-Varying Delays and Continuously Distributed Delays

Zhang

et al. 2017

Complexity

View full text Add to dashboard Cite

This paper studies the global exponential stability for a class of impulsive disturbance complex-valued Cohen-Grossberg neural networks with both time-varying delays and continuously distributed delays. Firstly, the existence and uniqueness of the equilibrium point of the system are analyzed by using the corresponding property of -matrix and the theorem of homeomorphism mapping. Secondly, the global exponential stability of the equilibrium point of the system is studied by applying the vector Lyapunov function method and the mathematical induction method. The established sufficient conditions show the effects of both delays and impulsive strength on the exponential convergence rate. The obtained results in this paper are with a lower level of conservatism in comparison with some existing ones. Finally, three numerical examples with simulation results are given to illustrate the correctness of the proposed results.

show abstract

Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays

Cited by 8 publications

References 47 publications

Mean Square Exponential Stability of Stochastic Complex‐Valued Neural Networks with Mixed Delays

Mean Square Exponential Stability of Stochastic Complex‐Valued Neural Networks with Mixed Delays

Mean-square exponential stability of fuzzy stochastic BAM networks with hybrid delays

Impulsive Disturbances on the Dynamical Behavior of Complex-Valued Cohen-Grossberg Neural Networks with Both Time-Varying Delays and Continuously Distributed Delays

Contact Info

Product

Resources

About